scholarly journals DISCRIMINANT ANALYSIS THROUGH FISHER’S METHOD OF THE LEVEL OF INTEREST IN TOURIST ATTRACTIONS IN INDONESIA

2020 ◽  
Vol 3 (2) ◽  
pp. 463
Author(s):  
Khairul Umam
Keyword(s):  
Discriminant Analysis ◽  
Tourist Attractions ◽  
Fisher's Method ◽  
Fisher’S Method

Penelitian ini bertujuan untuk mengelompokkan 100 tempat wisata yang ada di Indonesia kedalam daerah tempat wisata yang menarik dan tidak menarik untuk dikunjungi menggunakan software SPSS. Data tersebut dinormalkan menggunakan fungsi “sqrt” kemudian dianalisis menggunakan diskriminan fishers melalui SPSS. Data yang menjadi variabel dependen adalah keadaan menarik atau tidaknya suatu tempat wisata terhadap variabel-variabel yang mempengaruhi. Hasil yang diperoleh dari analisis ini berupa terdapatnya 50 tempat wisata yang menarik untuk dikunjungi dan 50 tempat wisata yang tidak menarik untuk dikunjungi. Fungsi diskriminan linier fisher yang terbentuk adalah. Tempat wisata yang menarik untuk dikunjungi: Y1 = 0,523X1 + (-3,610)X2 + 2,176X3 + 5,071X4 + 9,728X5 + 8,865X6 + (-0,440)X7 + 11,012X8 + 5,596X9 + 3,086X10 + (-0,881)X11 + 2,352X12 + (-1,251)X13 + 3,944X14 + 15,624X15 Dan tempat wisata yang tidak menarik untuk dikunjungi: Y2 = (-0,095)X1 + (-2,552)X2 + 2,081X3 + 3,130X4 + 6.602X5 + 5,330X6 + (-0,88)X7 + 8,333X8 + 3,276X9 + 3,907X10 + (-0,462)X11 + 2,246X12 + 0,214X13 + 3,842X14 + 9,042X15

Fisher's Method of Scoring

1992 ◽  
Vol 60 (1) ◽  
pp. 99 ◽  
Author(s):  
M. R. Osborne
Keyword(s):  
Fisher's Method ◽  
Fisher’S Method

scholarly journals A method to fuse multiphysics waveforms and improve predictive explosion detection: theory, experiment and performance

2020 ◽  
Vol 222 (2) ◽  
pp. 1195-1212 ◽  
Author(s):  
Joshua Carmichael ◽  
Robert Nemzek ◽  
Neill Symons ◽  
Mike Begnaud
Keyword(s):  
Constructive Method ◽  
Density Functions ◽  
Baseline Method ◽  
Waveform Data ◽  
Explosion Monitoring ◽  
Seismic Waveform ◽  
Transient Energy ◽  
Fisher's Method ◽  
And Performance ◽  
Fisher’S Method

SUMMARY Natural and human-made sources of transient energy often emit multiple geophysical signatures that include mechanical and electromagnetic waveforms. We present a constructive method to fuse and evaluate statistics that we derive from such multiphysics waveforms that improves our capability to detect small, near-ground explosions over similar methods that consume single signature waveforms. Our method advances Fisher's Combined Probability Test (Fisher's Method) to operate under both hypotheses of a binary test on noisy data and provide researchers with the density functions required to forecast the ability of Fisher's Method to screen fused explosion signatures from noise. We apply this method against 12 d, multisignature explosion and noise records to show (1) that a fused multiphysics waveform statistic that combines radio, acoustic and seismic waveform data can identify explosions roughly 0.8 magnitude units lower than an acoustic emission, STA/LTA detector for the same detection probability and (2) that we can quantitatively predict how this fused, multiphysics statistic performs with Fisher's Method. Our work thereby offers a baseline method for predictive waveform fusion that supports multiphenomenological explosion monitoring (multiPEM) and is applicable to any binary testing problem in observational geophysics.

Using Fisher's Method to Identify Enriched Gene Sets

2014 ◽  
Vol 6 (2) ◽  
pp. 154-162 ◽  
Author(s):  
Volha Tryputsen ◽  
Javier Cabrera ◽  
An De Bondt ◽  
Dhammika Amaratunga
Keyword(s):  
Gene Sets ◽  
Fisher's Method ◽  
Fisher’S Method

scholarly journals Tourism Mix Factor: Tourists’ Travel Orientation in Choosing Types of Tourism Objects in Indonesia

2020 ◽  
Vol 16 (5) ◽  
pp. 57
Author(s):  
Suliyanto Suliyanto
Keyword(s):  
Discriminant Analysis ◽  
Positive Attitude ◽  
Descriptive Analysis ◽  
Analytical Tool ◽  
Travel Agency ◽  
Tourist Attractions ◽  
Travel Agents ◽  
Stepwise Method ◽  
Survey Approach ◽  
Transportation Facilities

The purpose of this research is to analyze the tourism mix factor that differentiates tourists in choosing the type of tourism object as a basis for formulating strategies to attract tourists. This research is a quantitative study using a survey approach. The sample in this study was 200 respondents, consisting of 100 respondents of natural tourism visitors and 100 respondents of artificial tourism visitors. The analytical tool used in this study was descriptive analysis and discriminant analysis with the Stepwise method. The results of this study indicate that the variables that differentiate tourists from choosing natural tourism objects and artificial tourism objects are coolness, advertisements, facilities, prices, locations, travel agents, tourist attractions, transportation facilities and infrastructure, and public hospitality. Tourists who choose natural tourism objects have more positive attitude concerned with the variables of coolness, price, public hospitality, the existence of a travel agency, and the availability of transportation facilities and infrastructure, while visitors who choose artificial tourism objects have more positive attitude or are more concerned with location variables, advertisements, tourist attractions, and completeness of the facilities. This study provides a clear guidance on the tourism mix factor that distinguishes domestic tourists in Indonesia which is still very limited and need further investigation.

scholarly journals Reanalyzing Head et al. (2015): investigating the robustness of widespread p-hacking

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3068 ◽  
Author(s):  
Chris H.J. Hartgerink
Keyword(s):  
Data Analysis ◽  
Entire Range ◽  
Statistical Significance ◽  
Value Distribution ◽  
Large Collection ◽  
Fisher's Method ◽  
Fisher’S Method

Head et al. (2015) provided a large collection of p-values that, from their perspective, indicates widespread statistical significance seeking (i.e., p-hacking). This paper inspects this result for robustness. Theoretically, the p-value distribution should be a smooth, decreasing function, but the distribution of reported p-values shows systematically more reported p-values for .01, .02, .03, .04, and .05 than p-values reported to three decimal places, due to apparent tendencies to round p-values to two decimal places. Head et al. (2015) correctly argue that an aggregate p-value distribution could show a bump below .05 when left-skew p-hacking occurs frequently. Moreover, the elimination of p = .045 and p = .05, as done in the original paper, is debatable. Given that eliminating p = .045 is a result of the need for symmetric bins and systematically more p-values are reported to two decimal places than to three decimal places, I did not exclude p = .045 and p = .05. I conducted Fisher’s method .04 < p < .05 and reanalyzed the data by adjusting the bin selection to .03875 < p ≤ .04 versus .04875 < p ≤ .05. Results of the reanalysis indicate that no evidence for left-skew p-hacking remains when we look at the entire range between .04 < p < .05 or when we inspect the second-decimal. Taking into account reporting tendencies when selecting the bins to compare is especially important because this dataset does not allow for the recalculation of the p-values. Moreover, inspecting the bins that include two-decimal reported p-values potentially increases sensitivity if strategic rounding down of p-values as a form of p-hacking is widespread. Given the far-reaching implications of supposed widespread p-hacking throughout the sciences Head et al. (2015), it is important that these findings are robust to data analysis choices if the conclusion is to be considered unequivocal. Although no evidence of widespread left-skew p-hacking is found in this reanalysis, this does not mean that there is no p-hacking at all. These results nuance the conclusion by Head et al. (2015), indicating that the results are not robust and that the evidence for widespread left-skew p-hacking is ambiguous at best.

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